A data science team stores transaction features in a SageMaker Feature Store offline store, continuously ingesting new feature records with event timestamps. To train a fraud model, they must build a training dataset that reflects each labeled event's feature values as they existed at the moment of that event, avoiding leakage of future feature updates. Which approach correctly produces this training dataset?